Apparatus and method with in-memory delay dependent processing

ABSTRACT

An in-memory processing apparatus includes: a memory cell array comprising memory cell groups configured to generate current sums of column currents flowing through respective column lines in response to input signals input through row lines; voltage controlled delay circuits configured to output, in response to an input of a start signal at a first time point, stop signals at second time points delayed by delay times determined based on magnitudes of applied sampling voltages corresponding to the current sums; a time-digital converter configured to perform time-digital conversion at the second time points; and sampling resistors connected to the column lines, wherein the time-digital converter is configured to reset a counter at the first time point, and output counting values as digital values at the second time points.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation Application of U.S. PatentApplication No. 17/150,891 filed on Jan. 15, 2021, which claims thebenefit under 35 U.S.C. § 119(a) of Korean Patent Application No.10-2020-0082260, filed on Jul. 3, 2020, in the Korean IntellectualProperty Office, the entire disclosure of which is incorporated hereinby reference for all purposes.

BACKGROUND 1. Field

The following description relates to an apparatus with in-memoryprocessing and a computing apparatus including the same.

2. Description of Related Art

A neural network may be a computing system implemented with reference toa computational architecture. Input data may be analyzed and validinformation may be extracted using neural networks in various types ofelectronic systems. Processing in neural networks may require a largeamount of computations on complex input data. As the data of a neuralnetwork increases and the connectivity of the architecture constitutingthe neural network becomes complicated, the amount of computations andthe frequency of memory access in a processing apparatuses may increaseexcessively, and as a result, miniaturization and commercialization ofprocessing apparatuses may be inefficient. For example, processing ofthe neural network may include a multiply-accumulate (MAC) operationthat repeats multiplication and addition. However, hardware architectureand hardware driving methods may not efficiently process repetitive MACoperations that take up a large amount of computations in the processingof neural networks at low power and high speed.

SUMMARY

This Summary is provided to introduce a selection of concepts insimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, an in-memory processing apparatus includes: amemory cell array comprising memory cell groups configured to generatecurrent sums of column currents flowing through respective column linesin response to input signals input through row lines; voltage controlleddelay circuits configured to output, in response to an input of a startsignal at a first time point, stop signals at second time points delayedby delay times determined based on magnitudes of applied samplingvoltages corresponding to the current sums; and a time-digital converterconfigured to perform time-digital conversion at the second time points.

The voltage controlled delay circuits may be configured to determine thedelay times in proportion to the magnitudes of the sampling voltages.

The voltage controlled delay circuits may be current-starved typesincluding transistors to which the sampling voltages is are applied andinverters to which the start signal is input.

The voltage controlled delay circuits may include biases connected tothe column lines and configured to apply the sampling voltages,current-starved delay elements to which the start signals are input, andbuffers configured to output the stop signals.

The may include sampling resistors connected to the column lines,wherein the sampling voltages are applied to the sampling resistors.

The sampling resistors may be connected in series to the column lines,and the voltage controlled delay circuits may be connected in parallelto the sampling resistors.

Values of the sampling voltages may be determined based on compositeresistances of resistance values of the memory cell groups andresistance values of the sampling resistors.

The apparatus may include a driver configured to input the input signalsto the memory cell array at a third time point synchronized with thefirst time point.

The time-digital converter may be configured to reset a counter at thefirst time point, and output counting values as digital values at thesecond time points.

The time-digital converter may include an oscillator configured togenerate a pulse at the first time point and the counter configured tocount the pulse.

The time-digital converter may include flip-flops configured to latchthe counting values at the second time point.

In another general aspect, a computing apparatus includes: a hostprocessor; a memory device; and an in-memory processing devicecomprising: a memory cell array comprising memory cell groups configuredto generate current sums of column currents flowing through respectivecolumn lines in response to input signals input through row lines;voltage controlled delay circuits configured to output, in response toan input of a start signal at a first time point, stop signals at secondtime points delayed by delay times determined based on magnitudes ofapplied sampling voltages corresponding to the current sums; and atime-digital converter configured to perform time-digital conversion atthe second time points.

The voltage controlled delay circuits may be configured to determine thedelay times in proportion to the magnitudes of the sampling voltages.

The voltage controlled delay circuits may be current-starved typesincluding transistors to which the sampling voltages are input andinverters to which the start signal is input.

The voltage controlled delay circuits may include biases connected tothe column lines and configured to apply the sampling voltages,current-starved delay elements to which the start signals are input, andbuffers configured to output the stop signals.

The in-memory processing device may include sampling resistors connectedto the column lines, wherein the sampling voltages are applied to thesampling resistors.

The time-digital converter may be configured to reset a counter at thefirst time point, and output counting values as digital values at thesecond time points.

The memory device may store instructions that, when executed by the hostprocessor, configure the host processor to control the in-memoryprocessing device to perform the generating of the current sums, theoutputting of the stop signals, and the performing of the time-digitalconversion.

In another general aspect, an in-memory processing method includes:inputting input signals to memory cell groups through row lines of amemory cell array; applying sampling voltages corresponding to currentsums of column currents flowing through column lines of the memory cellarray to voltage controlled delay circuits connected to the columnlines; inputting a start signal to the voltage controlled delay circuitsat a first time point; outputting stop signals at second time pointsdelayed by delay times determined based on magnitudes of the samplingvoltages; and performing time-digital conversion at the second timepoints.

Sampling resistors may be connected to the column lines, and the methodmay include determining the sampling voltages based on compositeresistances of resistance values of the memory cell groups andresistance values of the sampling resistors.

The method may include resetting a counter at the first time point, andoutputting counting values as digital values at the second time points.

The method may include outputting, based on the output counting values,an output digital value corresponding to a MAC operation result of aneural network.

In another general aspect, an in-memory processing apparatus includes: amemory cell array comprising memory cell groups each corresponding to arespective column line and configured to generate a current sum ofcolumn currents flowing through the respective column line in responseto input signals applied through row lines; voltage controlled delaycircuits each corresponding to a respective column line and configuredto output, in response to an input of a start signal at a first timepoint, a stop signal at second time point delayed by a delay timedetermined based on a magnitude of an applied sampling voltagecorresponding to a respective one of the current sums; and atime-digital converter configured to, based on the stop signals, performtime-digital conversion at the second time points.

The time-digital converter may include: an oscillator configured togenerate a counting pulse starting from the first time point, inresponse to receiving the start signal; a counter configured to generatecounting values of the counting pulse in response to the generating ofthe counting pulse; and flip-flops each corresponding to a respectivecolumn line and configured to output a counting value of the generatedcounting values corresponding to a respective second time point, inresponse to receiving a stop signal of the stop signals from arespective one of the voltage controlled delay circuits.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a neural network node model.

FIG. 2 illustrates an example of a two-dimensional array circuit forperforming a neuromorphic operation.

FIG. 3 illustrates an example of a method of processing a neuromorphicoperation.

FIG. 4 illustrates an example of an in-memory processing device.

FIG. 5 illustrates an example of an in-memory processing device.

FIG. 6 illustrates an example of an operation of a voltage controlleddelay line in an in-memory processing device.

FIG. 7 illustrates an example of an operation of a voltage controlleddelay line in an in-memory processing device.

FIG. 8 illustrates an example of time-digital conversion performed in anin-memory processing device.

FIGS. 9A to 9F illustrate examples of signals output from respectivecomponents as a start signal is input to an in-memory processing device.

FIG. 10 illustrates an example of a method of performing in-memoryprocessing.

FIG. 11 illustrates an example of a computing apparatus.

FIG. 12 illustrates an example of a neural network.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known in the art, after anunderstanding of the disclosure of this application, may be omitted forincreased clarity and conciseness.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings, wherein like referencenumerals refer to like elements throughout. In this regard, the one ormore embodiments may have different forms and should not be construed asbeing limited to the descriptions set forth herein. Accordingly, theembodiments are merely described below, by referring to the figures, toexplain aspects. As used herein, the term “and/or” includes any and allcombinations of one or more of the associated listed items. Expressionssuch as “at least one of,” when preceding a list of elements, modify theentire list of elements and do not modify the individual elements of thelist.

Unless otherwise defined, all terms, including technical and scientificterms, used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure pertains and afteran understanding of the disclosure of this application. Terms, such asthose defined in commonly used dictionaries, are to be interpreted ashaving a meaning that is consistent with their meaning in the context ofthe relevant art and the disclosure of this application, and are not tobe interpreted in an idealized or overly formal sense unless expresslyso defined herein.

The terminology used herein is for the purpose of describing particularexamples only, and is not to be used to limit the disclosure. As usedherein, the singular forms “a,” “an,” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. As used herein, the term “and/or” includes any one and anycombination of any two or more of the associated listed items. As usedherein, the terms “include,” “comprise,” and “have” specify the presenceof stated features, numbers, operations, elements, components, and/orcombinations thereof, but do not preclude the presence or addition ofone or more other features, numbers, operations, elements, components,and/or combinations thereof. The use of the term “may” herein withrespect to an example or embodiment (for example, as to what an exampleor embodiment may include or implement) means that at least one exampleor embodiment exists where such a feature is included or implemented,while all examples are not limited thereto.

Hereinafter, embodiments will be described in detail with reference tothe accompanying drawings. However, the embodiments may be implementedin many different forms and should not be construed as being limited tothe embodiments set forth herein.

FIG. 1 illustrates an example of a neural network node model.

The neural network node model 11 may implement a neuromorphic operationincluding a multiplication operation that multiplies information from aplurality of neurons by a synaptic weight, an addition operation Σ onvalues ω₀x₀, ω₁x₁, and ω₂x₂ multiplied by the synaptic weight, and anoperation that applies the characteristic function b and the activationfunction f to the result of the addition operation. Neuromorphicoperation results may be provided by the neuromorphic operation. Here,values such as x₀, x₁, x₂, . . . may be referred to as axon values, andvalues such as ω₀, ω₁, ω₂, . . . may be referred to as synaptic weights.While the nodes, values, and weights of the neural network node model 11may be respectively referred to as “neurons,” “axon values,” and“synaptic weights,” such reference is not intended to impart anyrelatedness with respect to how the neural network architecturecomputationally maps or thereby intuitively recognizes information andhow a human's neurons operate. I.e., the terms are merely terms of artreferring to the hardware implemented nodes, values, and weights of theneural network node model 11.

FIG. 2 illustrates an example of a two-dimensional array circuit (e.g.,a two-dimensional array circuit 20) for performing a neuromorphicoperation.

Referring to FIG. 2 , the two-dimensional array circuit 20 may includean axon circuit group 210 including N axon circuits A₁ to A_(N), aneuron circuit group 230 including M neuron circuits N₁ to N_(M), and asynapse array 220 including N*M synapses S₁₁ to S_(NM), wherein N and Mare arbitrary natural numbers, respectively. While the circuits andarrays may be referred to as “axon circuits,” “neuron circuits” and/or“synapse arrays,” such terms are merely terms of art referring to thehardware-implemented array circuit.

Each of the synapses S₁₁ to S_(NM) of the synapse array 220 may bearranged at intersections where the first direction lines extending in afirst direction from the axon circuits A₁ to A_(N) of the axon circuitgroup 210 intersect with the second direction lines extending in asecond direction from the neuron circuits N₁ to N_(M) of the neuroncircuit group 230. Here, for convenience of description, it isillustrated that the first direction is a row direction and the seconddirection is a column direction, but the present disclosure is notlimited thereto. For example, the first direction may be the columndirection, and the second direction may be the row direction.

Each of the axon circuits A₁ to A_(N) of the axon circuit group 210 mayreceive (for example, each of axons a₁, a₂, . . . , a_(N)) to transmitan activation to the first direction lines. The activation maycorrespond an electrical signal input to each of the axon circuits A₁ toA_(N) of the axon circuit group 210. Each of the axon circuits A₁ toA_(N) of the axon circuit group 210 may include a memory, a register, ora buffer for storing input information. Meanwhile, the activation may bea binary activation having a binary value. For example, the binaryactivation may include 1-bit information corresponding to the logicalvalue “0” or “1” (or logical value “−1” or “1”). However, the activationis not limited thereto and may have a ternary value or a multi-bitvalue.

Each of the synapses S₁₁ to S_(NM) of the synapse array 220 may be acircuit that stores synaptic weights (e.g., such as that of the neuralnetwork node model 11 of FIG. 1 ) corresponding to interconnectionintensity between a neuron and another neuron. In FIG. 2 , forconvenience of description, w₁, w₂, . . . , w_(M) are illustrated asexamples of synaptic weights to be stored in each synapse, but othersynaptic weights may be stored in each synapse. Each of the synapses S₁₁to S_(NM) of the synapse array 220 may include a memory device forstoring synaptic weights or may be connected to another memory devicethat stores synaptic weights. Here, such a memory device may include,for example, a memristor or a resistive memory cell. The memristor orresistive memory cell may be implemented with static random-accessmemory (SRAM), phase change memory (PCM), oxide based memory (OXRAM),magnetoresistive random-access memory(MRAM), spin-transfer torquerandom-access memory (STT-RAM), conductive-bridge random-access memory(CBRAM), resistive RAM (RRAM), ferroelectric RAM (FRAM), a magnetictunnel junction (MTJ) device, etc., but is not limited thereto.

Each of the synapses S₁₁ to S_(NM) of the synapse array 220 may receivethe activation input transmitted from each of the axon circuits A₁ toA_(N) of the axon circuit group 210 through the corresponding firstdirection line and may output a result of a neuromorphic operationbetween the stored synaptic weight and the activation input. Forexample, the neuromorphic operation between the synaptic weight and theactivation input may be a multiplication operation (i.e., an ANDoperation), but is not limited thereto. That is, the result of theneuromorphic operation between the synaptic weight and the activationinput may be a value obtained by another arbitrary appropriate operationto simulate the intensity or magnitude of the activation adjustedaccording to the interconnection intensity between a neuron and anotherneuron.

According to the neuromorphic operation between the synaptic weight andthe activation input, the magnitude or intensity of signals transmittedfrom the axon circuits A₁ to A_(N) of the axon circuit group 210 to theneuron circuits N₁ to N_(M) of the neuron circuit group 230 may beadjusted. In this way, an operation in which the magnitude or intensityof the signal transmitted to the next neuron is adjusted according tothe intensity of the interconnection between a neuron and another neuronmay be simulated using the synapses S₁₁ to S_(NM) of the synapse array220.

Each of the neuron circuits N₁ to N_(M) of the neuron circuit group 230may receive the result of a neuromorphic operation between the synapticweight and the activation input through the corresponding seconddirection line. Each of the neuron circuits N₁ to N_(M) of the neuroncircuit group 230 may determine whether to output a spike based on theresult of the neuromorphic operation. For example, each of the neuroncircuits N₁ to N_(M) may output a spike when the accumulated value ofthe neuromorphic operation results is equal to or greater than a presetthreshold. The spikes output from the neuron circuits N₁ to N_(M) of theneuron circuit group 230 may correspond to activations to be input toaxon circuits of a next step.

Meanwhile, the neuron circuits N₁ to N_(M) of the neuron circuit group230 are located in the next step based on the synapses S₁₁ to S_(NM) ofthe synapse array 220 and thus may be referred to as post-synapticneuron circuits, and the axon circuits A₁ to A_(N) of the axon circuitgroup 210 are located in the previous step based on synapses S₁₁ toS_(NM) of the synapse array 220 and thus may be referred to aspre-synaptic neuron circuits.

FIG. 3 illustrates an example of a method of processing a neuromorphicoperation in an in-memory processing device.

A two-dimensional array circuit for processing neuromorphic operationsmay use a current summation method for each column line. For example,the two-dimensional array circuit may sum the currents flowing along thecolumn line 310 through synapses S₁₁, S₂₁, S_((N-1)1), and S_(N1) byactivations transmitted from axon circuits A₁ to A_(N), and outputs aspike when the magnitude or intensity of the summed current is greaterthan or equal to a preset threshold. At this time, as the spike outputis quantized into a digital signal and then applied as an activationinput to be input to the axon circuits of a next step, a peripheralcircuit such as an analog to digital converter (ADC) may be used.However, when independent ADCs are provided in all columns as in aconventional in-memory processing device, power consumption increasesand a large area is required in the circuit. In contrast, for aneuromorphic operation such as a MAC operation that repeats addition andmultiplication, an in-memory processing device of one or moreembodiments may include a single highly integrated on-chip system havinga circuit design based on a single time-digital converter (TDC), withoutconfiguring individual ADCs in columns as in the conventional in-memoryprocessing device. Hereinafter, a non-limiting example method ofimplementing the above system will be described.

FIG. 4 illustrates an example of an in-memory processing device (e.g.,an in-memory processing device 100).

Referring to FIG. 4 , the in-memory processing device 100 may beconfigured as a circuit that outputs a result of performingmultiplication and addition for a neuromorphic operation.

The in-memory processing device 100 may include a memory cell group 110including memory cells R₁, R₂, R₃, . . . , R_(m), a resistor R, avoltage controlled delay line (VCDL) 130, and a time-digital converter(TDC) 140.

FIG. 4 shows, for convenience of description, one column line 120 amongcolumn lines and row lines corresponding to a portion of the memory cellarray provided in the in-memory processing device 100. Accordingly, thememory cell array of the in-memory processing device 100 may include amemory cell group 110 for each of the column lines of the memory cellarray, and each memory cell group 110 may include memory cells arrangedat positions where the corresponding column line intersects with the rowlines.

As described above, memory cells of the memory cell group 110 may beimplemented as a memristor or a resistive memory device, and may be adevice having a variable resistance. Voltage may be applied to thememory cells of the memory cell group 110 through each row line inresponse to input signals b₁, b₂, b₃, . . . , b_(m) (e.g., where each ofthe input signals b₁, b₂, b₃, . . . , b_(m) may correspond to arespective row line). For example, to the memory cells of the memorycell group 110, input voltage signals, which are input signals, may bedirectly applied, or supply voltages may be applied by the inputsignals.

One end of each of the memory cells R₁, R₂, R₃, . . . , R_(m) of thememory cell group 110 may be configured to receive a voltage through aswitch group 101 including switches SW₁, SW₂, SW₃, . . . , SW_(m), andthe other end of each of the memory cells of the memory cell group 110may be connected to a resistor R and a voltage controlled delay line130. That is, to each column line 120 including the memory cell group110, the resistor R and the voltage controlled delay line 130 may beconnected.

According to a resistance value of each of the memory cells of thememory cell group 110 and a voltage value of an input signal applied toeach of the memory cells of the memory cell group 110, a current havinga current value calculated based on Ohm's law may flow through thecolumn line 120. Accordingly, the current sum lo of the column currentsflowing through the column line 120 may correspond to a result value ofa MAC operation between the memory cells of the memory cell group 110and input signals corresponding to each other.

The input signals (b₁, b₂, b₃, . . . , b_(m)) may be respectivelyapplied to the memory cell of the memory cell group 110 in response to astart signal START. To this end, the in-memory processing device 100 mayinclude the switch group 101 including switches SW₁, SW₂, SW₃, . . . ,SW_(m) that are switched by the start signal START. Ends of the switchesSW₁, SW₂, SW₃, . . . , SW_(m) of the switch group 101 may be connectedto ends of the memory cells R₁, R₂, R₃, . . . , R_(m) of the memory cellgroup 110, respectively. For example, the switch SW₁ may be connected toone end of a memory cell R₁, the switch SW₂ may be connected to one endof a memory cell R₂, the switch SW₃ may be connected to one end of amemory cell R₃, . . . , and the switch SW_(m) may be connected to oneend of a memory cell R_(m). Here, m is a natural number greater than orequal to 1. The other ends of the switches of the switch group 101 maybe connected to the input signals b₁, b₂, b₃, . . . , b_(m),respectively.

Meanwhile, the start signal START may be simultaneously input to thevoltage controlled delay line 130 and the time-digital converter 140.Alternatively, signals synchronized with the start signal START may beinput to the voltage controlled delay line 130 and the time-digitalconverter 140.

In an example, an input signal may not always be applied to a respectivememory cell of the memory cell group 110 in response to the start signalSTART, according to an input signal value of the input signal (i.e.,input voltage value). For example, the input signal may not be appliedwhen the input voltage of the input signal is 0, but is not limitedthereto and, in another example, the input signal may not be appliedwhen the input voltage is a specific voltage value (e.g., when the inputvoltage is equal to or below a predetermined threshold).

The input signals may correspond to individual bit values of an inputbit sequence composed of a series of binary values. Specifically, in thein-memory processing device 100, each of the row lines may correspond toeach bit position of the input bit sequence. When a bit value of acertain bit position is 1, an input signal having a voltage valuecorresponding to the bit value 1 may be applied to the row linecorresponding to the certain bit position. Further, when a bit value ofa certain bit position is 0, an input signal having a voltage value (forexample, 0 V voltage) corresponding to the bit value 0 may be applied tothe row line corresponding to the certain bit position.

The resistance value of each of the memory cells of the memory cellgroup 110 may have a bit value (for example, weight or synaptic weight)that is multiplied by each bit of the input bit sequence. Because thememory cells of the memory cell group 110 may be implemented as aresistive memory device having a variable resistance, a memory cellcorresponding to the bit value 1 of the memory cells of the memory cellgroup 110 may have a first resistance value, and a memory cellcorresponding to the bit value 0 of the memory cells of the memory cellgroup 110 may have a second resistance value. However, the memory cellsof the memory cell group 110 are not limited thereto and may beimplemented as a circuit in which a resistor corresponding to acorresponding bit value is selected from among a plurality of resistorshaving different resistance values by using a switching element.

Meanwhile, in the present embodiment, while the bit value may be 1 or 0,the bit value is not limited thereto, and may be 1 or −1, other binarybit values, ternary bit values, or the like.

The resistor R may be connected to the column line 120 to which thememory cells R₁, R₂, R₃, . . . , R_(m) of the memory cell group 110 areconnected, and a voltage V_(o) corresponding to the current sum I_(o) ofthe column currents flowing through the column line 120 may be appliedto the resistor R. Accordingly, in the in-memory processing device 100,the resistor R may constitute a sampling circuit for sampling a samplingvoltage Vo corresponding to the current sum 10 of the column line 120.

The sampling voltage V_(o) is a voltage corresponding to the current sumI_(o) of the column line 120 connected to the resistor R, and may be aMAC operation result between the resistance values of the memory cellsof the memory cell group 110 and the applied input signals.

In this case, the value of the sampling voltage V_(o) may be determinedbased on the composite resistance of the resistance value of the memorycells of the memory cell group 110 and the resistance value of thesampling resistor R. For example, the sampling resistor R may beconnected in series to each column line 120, and the value of thesampling voltage V_(o) applied to the sampling resistor R may bedetermined by a ratio of the resistance value of the memory cellsconnected to the column line 120 to the value of the sampling resistorR.

The voltage controlled delay line 130 or the voltage controlled delaycircuit may be a logic circuit using propagation delay characteristics,and may output the stop signal STOP after a preset delay time haselapsed after receiving a start signal START.

The sampling voltage V_(o) may be input to the voltage controlled delayline 130. The voltage controlled delay line 130 may control a length ofthe delay time based on the magnitude of the voltage value of thesampling voltage V_(o). For example, the length of the delay time may bedetermined in proportion to the sampling voltage V_(o).

The voltage controlled delay line 130 may be connected to the columnline 120 containing each of the memory cells of the memory cell group110. The voltage controlled delay line 130 may be connected to thesampling circuit of each column line 120. As a result, the voltagecontrolled delay line 130 may receive the sampling voltage V_(o) appliedto the sampling circuit. That is, the sampling resistor R may sample thesampling voltage V_(o) corresponding to the result of the MAC operation,and the sampling voltage V_(o) may be applied to the voltage controlleddelay line 130 connected to the sampling resistor R. For example, thevoltage controlled delay line 130 may be connected in parallel to thesampling resistor R to receive the sampling voltage V_(o) as it is.

Non-limiting examples of the voltage controlled delay line 130 will befurther described later in more detail with reference to FIGS. 6 and 7 .

When receiving the start signal START, the time-digital converter 140may reset a counter and restart counting for the number of pulses.

Thereafter, when receiving the stop signal STOP from the voltagecontrolled delay line 130, the time-digital converter 140 outputs acounting value T_(out) as a digital value at the time point when thestop signal STOP is received.

Meanwhile, the memory cells of the memory cell group 110, the resistorR, the voltage controlled delay line 130, and the time-digital converter140 shown in FIG. 4 may correspond to one output line (i.e., one columnline) on the memory cell array in the in-memory processing device 100.However, as described above, a plurality of output lines (i.e., columnlines) may be provided in the memory cell array, and in an example,there may be a respective memory cell group 110, resistor R, voltagecontrolled delay line 130, and time-digital converter 140 correspondingto each output line.

FIG. 5 illustrates an example of an in-memory processing device (e.g.,an in-memory processing device 50).

Previously, with respect to FIG. 4 , the input lines (row lines) and oneoutput line (column line 120) in the in-memory processing device 120have been described, but hereinafter, with reference to FIG. 5 , anin-memory processing device 50 that includes a memory cell array 590including input lines (row lines) and multiple output lines (columnlines) will be described.

The memory cell array 590 in the in-memory processing device 50 mayinclude input lines (row lines) configured to receive an input signaland output lines (column lines) that individually output an outputsignal. Each of the input lines (row lines) may intersect with theoutput lines (column lines). Although, in FIG. 5 , it is shown that theinput line (row line) and the output line (column line) intersect witheach other perpendicularly, the present invention is not limitedthereto.

Each of input signals (b₁, b₂, b₃, b₄, . . . , b_(j), . . . , b_(m)) anda corresponding switch that switches the application of the input signalaccording to the start signal START are connected to a respective inputline (row line). The input signals (b₁, b₂, b₃, b₄, . . . , b_(j), . . ., b_(m)) may correspond to input voltages or input currents representingbinary values, but are not limited thereto. For example, an input signalindicating bit value 1 may represent an arbitrary voltage, and an inputsignal indicating bit value 0 may represent a floating voltage.

Memory cell groups 510 are provided in the memory cell array, and eachof the memory cell group 510 includes memory cells provided at positionswhere the input lines (row lines) intersect with the correspondingoutput line (column line).

Each of the memory cells of the memory cell group 510 may be configuredto receive an input signal (input voltage) through an input line (rowline) in which the corresponding memory cell is arranged among the inputlines (row lines). For example, memory cells arranged along a j-th inputline 591 may be configured to receive a j-th input signal b_(j) inresponse to the start signal START.

According to an embodiment, the in-memory processing device 50 mayinclude a read/write (R/W) driver connected to the memory cell array.The R/W driver may control inputs of input signals (b₁, b₂, b₃, . . . ,b_(m)) by controlling operations of the switches connected to the memorycell array 590.

In addition, the R/W driver may input the start signal START to thevoltage controlled delay line 540 and the time-digital converter 550.The time point at which the start signal START is input to the voltagecontrolled delay line 540 may be synchronized and coincide with the timepoint at which the start signal START is input to the time-digitalconverter 550.

In an example, the time point at which the input signals (b₁, b₂, b₃, .. . , b_(m)) are input to the memory cell array 590 may be synchronizedand coincide with the time point at which the start signal START isinput to the voltage controlled delay line 540 and the time-digitalconverter 550.

In another example, the time point at which the input signals (b₁, b₂,b₃, . . . , b_(m)) are input to the memory cell array 590 and the timepoint at which the start signal START is input to the voltage controlleddelay line 540 and the time-digital converter 550 may be different fromeach other. For example, the time point at which the input signals areinput to the memory cell array 590 may be earlier than the time point atwhich the start signal START is input to the voltage controlled delayline 540 and the time-digital converter 550.

For example, a switch for controlling the current state may be arrangedat a position between an end of a column line of the memory cell array590 and the voltage controlled delay line 540. In this case, the R/Wdriver may control the time point at which a sampling voltage V_(n) isinput to the voltage controlled delay line 540 by controlling theoperation of the switch. Accordingly, the time point at which thesampling voltage V_(n) is input to the voltage controlled delay line 540and the time point at which the start signal START is input to thevoltage controlled delay line 540 may be different from each other.

The in-memory processing device 50 may include resistors 520 (e.g.,sampling resistors) and voltage controlled delay lines 540 connected toends of the output lines (column lines), a time-digital converter 550and an output unit 560.

The resistors 520 may be individually arranged for each output line(column line), and one resistor connected to a certain output line(column line) may constitute a sampling circuit that charges thesampling voltage V_(n) corresponding to the current sum of the certaincorresponding output line (column line).

The resistors 520 connected to the output lines (column lines) mayconstitute a sampling circuit and may have the same resistance values.Thus, the difference in time constant between the resistors 520 maydepend on a difference in composite resistance values of the outputlines (column lines), that is, a difference in the current sums of theoutput lines (column lines).

The voltage controlled delay lines 540 may receive the start signalSTART. The voltage controlled delay lines 540 may be individuallyarranged for each output line (column line). Each of the voltagecontrolled delay lines 540 may determine a delay time based on themagnitude of the sampling voltage V_(n) of the corresponding output line(column line). Each of the voltage controlled delay lines 540 may outputthe stop signal STOP to the time-digital converter 550 at a time pointdelayed by a delay time from the time point at which the start signalSTART is received.

The time-digital converter 550 may receive the start signal START, reseta counter, and count pulses. When a stop signal STOP is received fromone of the voltage controlled delay lines 540, the time-digitalconverter 550 may perform time-digital conversion at a time point whenthe stop signal STOP is received.

Specifically, the time-digital converter 550 may receive the stop signalSTOP from each of the voltage controlled delay lines 540 connected toeach of the output lines (column lines). When a stop signal STOP amongthe stop signals is received, the time-digital converter 550 may latch acounting value of a counting pulse at a time point when the stop signalSTOP is received.

For example, a voltage controlled delay line corresponding to an i-thoutput line (i-th column line) 592 among the voltage controlled delaylines 540 may output a stop signal STOP_(i) to the time-digitalconverter 550 after a delay time has elapsed. When the stop signalSTOP_(i) is received, the time-digital converter 550 may latch acounting value (T_(out, i)) of a counting pulse at a time point when thestop signal STOP is received.

The output unit 560 may output a counting value for the certain outputline (column line) output from the time-digital converter 550 as adigital value OUT. Here, the output digital value OUT is a value derivedfrom the sampling voltage V_(n), and eventually corresponds to theresult of the MAC operation of the corresponding output line (columnline).

FIG. 6 illustrates an example of an operation of a voltage controlleddelay line (e.g., a voltage controlled delay line 600) in an in-memoryprocessing device.

Referring FIG. 6 , the voltage controlled delay line 600 may generate astop signal STOP_(n) delayed compared to the start signal START by adelay time determined according to the sampling voltage V_(n). Forexample, the voltage controlled delay line 600, after receiving thestart signal START at a first start time point T_(start1), may output astop signal STOP_(n) at a first stop time point T_(stop1) which isdelayed by a first delay time T_(delay1) determined by a first samplingvoltage V₁.

The voltage controlled delay line 600, after receiving the start signalSTART at a second start time point T_(start2), may output a stop signalSTOP_(n) at a second stop time point T_(stop2) which is delayed by asecond delay time T_(delay2) determined by a second sampling voltage V₂.

In this case, as the first sampling voltage V₁ having a magnitude largerthan the second sampling voltage V₂ is input, the first delay timeT_(delay1) may be set longer than the second delay time T_(delay2). Forexample, a length of a delay time may be determined to be in proportionto a respective sampling voltage.

The delay time may be adjusted according to a design of the voltagecontrolled delay line 600. The delay time of the voltage controlleddelay line 600 may be set considering the circuit of the in-memoryprocessing device, and may be experimentally selected as an optimalvalue (e.g., while also proportional to the respective samplingvoltage).

FIG. 7 illustrates an example of an operation of a voltage controlleddelay line (e.g., a voltage controlled delay line 700) in an in-memoryprocessing device.

Referring to FIG. 7 , the voltage controlled delay line 700 or a voltagecontrolled delay circuit may receive the sampling voltage V_(n).

The voltage controlled delay line 700 may control, based on themagnitude of the voltage value of the sampling voltage V_(n), the lengthof a delay time from the time point when the start signal START is inputto the time point when the stop signal STOP_(n) is output. For example,as the sampling voltage Vn increases, the delay time may increase.Alternatively, according to a design of the voltage controlled delayline, the delay time may decrease as the sampling voltage V_(n)increases.

A conventional inverter may also have a delay time from receiving aninput signal to generating an output signal. However, because the delaytime of the conventional inverter has a nonlinear relationship with thevoltage applied to the conventional inverter, the conventional invertermay not precisely control the delay time. In contrast, the voltagecontrolled delay line 700 of one or more embodiments may preciselycontrol the delay time because the relationship between the samplingvoltage and the delay time tends to be linear.

A logic circuit shown in FIG. 7 may be a current-starved type voltagecontrolled delay line, and may be an embodiment of a voltage controlleddelay line.

The voltage controlled delay line 700 may include a bias circuit 710, atleast one delay element circuit 720 and 730, a buffer circuit 740, etc.According to one or more embodiments, the voltage controlled delay line700 may further include another inverter connected in parallel to thedelay element circuit or a symmetric load connected to the delay elementcircuit.

The bias circuit 710 may apply the sampling voltage V_(n) to atransistor 722 of the delay element circuit 720 through a transistor 712connected to each column line of the memory cell array. According to adesign, the transistor 712 and a transistor 714 may be arranged in theform of a current mirror.

The delay element circuit 720 may include an inverter 724 andtransistors 722 and 726. The transistors 722 and 726 may receive thesampling voltage V_(n) from the bias circuit 710. The inverter 724 mayreceive the start signal START.

The transistor 712 may act as a current sink, and the transistor 726 mayact as a current source, thereby limiting a peak current of the inverter724. In other words, the inverter 724 may be current-starved.

An output signal of inverter 724 may be input to the buffer circuit 740,and the buffer circuit 740 may output the stop signal STOP_(n).

According to embodiments, the delay element circuit 720 and the delayelement circuit 730 may include a plurality of delay element circuits,respectively, and may be arranged in cascade. The amplification ratiosbetween the transistors 712 and 714 of the bias circuit 710 and thetransistors 722 and 726 of the delay element circuit 720 may be designeddifferently.

The delay time may be adjusted according to a configuration of thevoltage controlled delay line 700. The delay time of the voltagecontrolled delay line 700 may be set considering the circuit of thein-memory processing device, and may be experimentally selected as anoptimal value. For example, according to the number of layers of delayelement circuits 720 and 730 arranged in cascade within the voltagecontrolled delay line 700 and amplification ratios between transistors,the length of the delay time that occurred by the same voltage value maybe adjusted.

According to embodiments, the voltage controlled delay line may includeother types of circuits using propagation delay characteristics. Forexample, the voltage controlled delay line may include a logic circuitincluding a shunt capacitor. The voltage controlled delay line using theshunt capacitor may include a capacitive loaded inverter and transistorsthat control charging and discharging of a load capacitor by acting aslinear resistors.

The voltage controlled delay line 700 may be simple logic circuit thatdoes not require a comparator or the like. Accordingly, there is anadvantage of minimizing power consumption and occupied area.

FIG. 8 illustrates an example of time-digital conversion performed in anin-memory processing device.

Referring to FIG. 8 , a time-digital converter 800 may include anoscillator 810, a counter 820, and flip-flops 832, 834, and 836.

The time-digital converter 800 may receive the start signal START. As orwhen the time-digital converter 800 receives the start signal START, theoscillator 810 may generate a counting pulse, and the counter 820 mayrestart counting for the counting pulse generated by the oscillator 810after being reset. Accordingly, when the start signal START is input tothe memory cell array, the counter 820 may be synchronized with theinput start signal START and may start counting the counting pulse.

The time-digital converter 800 may receive the stop signal STOP_(n) fromthe voltage controlled delay line. The flip-flops 832, 834, and 836 maybe connected to each of the voltage controlled delay lines.

The flip-flops 832, 834, and 836 may be enabled by a counting pulsereceived from the counter 820.

When the stop signal STOP_(n) is received from each voltage controlleddelay line while the counting pulse is being applied, each of therespective flip-flops 832, 834, and 836 latches the current countingvalue T_(out_n) at the time point at which the stop signal STOP_(n) isreceived.

As described above, each of the flip-flops 832, 834, and 836 provided inthe time-digital converter 800 may perform the time-digital conversionfor each of the column lines (output lines) by individually outputting acounting value T_(out_n) corresponding to the time point at which thestop signal STOP_(n) is received.

FIGS. 9A to 9F illustrate examples of signals output from respectivecomponents as a start signal is input to an in-memory processing device.

FIG. 9A is a diagram regarding a start signal START applied to anin-memory processing device. The start signal START may initiate the MACoperation of the memory cell array. Also, the start signal START may beinput to the voltage controlled delay line (and/or the time-digitalconverter).

FIG. 9B is a diagram regarding a signal of a sampling voltage V_(n). Thesampling voltage V_(n) may be applied to the sampling resistor connectedto the end of each column line through a MAC operation after the inputsignal is input to the memory cell array by the start signal START.Also, the signal of the sampling voltage V_(n) may be input to thevoltage controlled delay line.

For example, according to the result of the MAC operation, the magnitudeof the second sampling voltage V₂ generated after an input time point ofthe second start signal T_(start2) may be less than the magnitude of thefirst sampling voltage V₁ generated after an input time point of thefirst start signal T_(start1).

FIG. 9C is a diagram regarding a stop signal STOP_(n). The stop signalSTOP_(n) may be output at the time points T_(stop1) and T_(stop2)delayed by a delay time T_(delay1) and T_(delay2) from the time pointT_(start1) and T_(start2) when the start signal START is input to thevoltage controlled delay line. In this case, the delay times may bedetermined based on the magnitude of the sampling voltage.

For example, the second delay time T_(delay2) delayed according to thesecond sampling voltage V₂ may be less than the first delay timeT_(delay1) delayed according to the first sampling voltage V₁.

FIG. 9D is a diagram regarding a counting pulse generated by anoscillator of a time-digital converter (TDC). When the start signalSTART is applied, the oscillator may generate a counting pulse.

FIG. 9E is a diagram regarding counting for counting pulses by a counterof a TDC. When the start signal is applied, the counter may reset thecounting value and restart counting.

FIG. 9F is a diagram regarding an output value of a flip-flop of theTDC. The flip-flop may output a counting value OUT_(n) at the time pointwhen the stop signal STOP_(n) is received from each voltage controlleddelay line. For example, the flip-flop may output 0100 as a result ofthe MAC operation according to the first start signal, and the flip-flopmay output 0011 as a result of the MAC operation according to the secondstart signal.

The processing circuit in the in-memory processing device may performthe MAC operation for each column line (output line) throughtime-digital conversion as described above. In the in-memory processingdevice according to the present embodiments, compared to a Von Neumannstructure in which a memory and an operation unit are separated, datatransfer speed and power consumption may be improved. In addition,because the in-memory processing device does not need to haveanalog-to-digital converters (ADCs) for individual column lines, powerconsumption and an occupied area in a circuit may be reduced compared toan architecture equipped with ADCs.

Meanwhile, in the above embodiments, while the voltage value of theinput signal and the resistance value of the memory cell may be binaryvalues each composed of a value corresponding to ON (or logic “1”) and avalue corresponding to OFF (or logic “0”), the present embodiments arenot limited thereto. The voltage value of the input signal and theresistance value of the memory cell may have values distinguished bymulti-states. For example, when a value of 2 bits is input to one inputline (row line), the input signal (input voltage) may be floated for“00”, a first voltage value may be allocated as an input signal for“01”, a second voltage value greater than the first voltage value may beallocated as input signal for “10”, and a third voltage value greaterthan the second voltage value may be allocated as input signal for “11”.In addition, when the memory cell indicates a value of 2 bits, a firstresistance value, a second resistance value greater than the firstresistance value, a third resistance value, and a fourth resistancevalue may be allocated to the memory cell for “00”, “01”, “10”, and“11”, respectively. The input signal received by each input line (rowline) and each memory cell are not limited to indicating a 2-bitmulti-state, but may indicate a multi-state corresponding to more bits.Alternatively, values according to systems other than the binary systemmay be allocated.

FIG. 10 illustrates an example of a method of performing in-memoryprocessing. Because the method of performing the in-memory processingregarding FIG. 10 relates to the embodiments described with reference tothe drawings described above, descriptions given with reference to thedrawings described above may be applied to the method in FIG. 10 eventhough such repeated description is omitted below.

In operation 1001, according to application of the start signal START,an input signal may be applied to memory cells through each row line ofthe memory cell array. Also, the start signal START may be input to thevoltage controlled delay line. In addition, when the start signal STARTis applied, the counter of the TDC may be reset.

In operation 1002, a sampling voltage corresponding to the current sumof column currents flowing through each column line of the memory cellarray may be applied to the voltage controlled delay line connected toeach column line.

A sampling resistor may be connected to each column line so that asampling voltage of the column current is applied. That is, the samplingresistor may sample the sampling voltage corresponding to the result ofthe MAC operation, and the sampling voltage may be applied to thevoltage controlled delay line connected to the sampling resistor.

In operation 1003, after the start signal START is input to the voltagecontrolled delay circuit, the stop signal STOP_(n) delayed by a delaytime determined based on the magnitude of the sampling voltage may beoutput.

In operation 1004, the time-digital converter TDC may performtime-digital conversion that is outputting a current counting value ofthe counting pulse at the time point when the stop signal STOP_(n) isgenerated. Accordingly, a counting value corresponding to the result ofthe MAC operation for the input signal may be output.

FIG. 11 illustrates an example of a computing apparatus (e.g., acomputing apparatus 1100).

Referring to FIG. 11 , the computing apparatus 1100 may extract validinformation by analyzing input data in real time based on a neuralnetwork, and based on the extracted information, determine a situationor control components of an electronic device on which the computingapparatus 1100 is mounted. For example, the computing apparatus 1100 maybe, or be applied to, a robot device such as a drone and an advanceddrivers assistance system (ADAS), a smart TV, a smart phone, a medicaldevice, a mobile device, an image display device, a measurement device,an IoT device, and the like, and may be mounted on at least one ofvarious types of electronic devices.

The computing apparatus 1100 may include a host processor 1110 (e.g.,one or more processors), a RAM 1120, an in-memory processing device1130, a memory device 1140, a sensor module 1150, and a communicationmodule 1160. The computing apparatus 1100 may further include aninput/output module, a security module, and a power control device. Someof hardware components of the computing apparatus 1100 may be mounted onat least one semiconductor chip. The in-memory processing device 1130 isan apparatus including the in-memory processing device described withreference to the drawings described above, and may correspond to aneural network dedicated hardware accelerator itself or a neural networkapparatus including the same.

The host processor 1110 may control some or all operations of thecomputing apparatus 1100. The host processor 1110 may include a singleprocessor core or may include multiple processor cores. The hostprocessor 1110 may process or execute programs and/or data stored in thememory device 1140. The host processor 1110 may control functions of thein-memory processing device 1130 by executing programs stored in thememory device 1140. The host processor 1110 may be implemented by acentral processing unit (CPU), a graphics processing unit (GPU), or anapplication processor (AP).

The RAM 1120 may temporarily store programs, data, or instructions. Forexample, programs and/or data stored in the memory device 1140 may betemporarily stored in the RAM 1120 under the control of the hostprocessor 1110 or boot code. The RAM 1120 may be implemented by a memorysuch as dynamic RAM (DRAM) or static RAM (SRAM).

The in-memory processing device 1130 may perform the neuromorphicoperation described above with respect to the drawings, for example, aMAC operation, and output the result of the MAC operation. However, thein-memory processing device 1130 may also perform various in-memorycomputing.

The memory device 1140 is a storage location for storing data, and maystore an operating system (OS), various programs, and various data. Inan embodiment, in the memory device 1140, data (for example, inputsignal data, weight data, etc.) required for the operation of thein-memory processing device 1130 and operation result data (for example,MAC operation results, etc.) may be stored.

The memory device 1140 may be DRAM, but is not limited thereto. Thememory device 1140 may include at least one of a volatile memory and anonvolatile memory. The nonvolatile memory includes read only memory(ROM), programmable ROM (PROM), erasable programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory,phase-change RAM (PRAM), magnet RAM (MRAM), RRAM, ferroelectric RAM(FRAM), and the like. The volatile memory includes DRAM, SRAM,synchronous DRAM (SDRAM), PRAM, MRAM, RRAM, FeRAM, and the like. In anembodiment, the memory device 1140 may include at least one of a harddisk drive (HDD), a solid state drive (SSD), a compact flash (CF) card,a secure digital (SD) card, a micro-SD card, a mini-SD card, an extremedigital (xD) card, or a memory stick.

The sensor module 1150 may collect information around an electronicdevice on which the computing apparatus 1100 is mounted. The sensormodule 1150 may sense or receive a signal (for example, an image signal,an audio signal, a magnetic signal, a bio signal, a touch signal, etc.)from the outside of the electronic device, and convert the sensed orreceived signal into data. To this end, the sensor module 1150 mayinclude at least one of various types of sensing devices such as amicrophone, an imaging device, an image sensor, a light detection andranging (LIDAR) sensor, an ultrasonic sensor, an infrared sensor, a biosensor, a touch sensor, etc.

The sensor module 1150 may provide the data obtained from the sensed orreceived signal to the in-memory processing device 1130 as input data.For example, the sensor module 1150 may include an image sensor,generate a video stream by photographing an external environment of theelectronic device, and provide in order a continuous data frame of thevideo stream to the in-memory processing device 1130 as input data.However, the present invention is not limited thereto, and the sensormodule 1150 may provide various types of data to the in-memoryprocessing device 1130.

The communication module 1160 may include various wired or wirelessinterfaces capable of communicating with external devices. For example,the communication module 1160 may include a communication interfaceaccessible to a wired local area network (LAN), a wireless local areanetwork (WLAN) such as a wireless fidelity (Wi-Fi), a wireless personalarea network (WPAN) such as a Bluetooth network, a wireless universalserial bus (USB), ZigBee, near field communication (NFC),radio-frequency Identification (RFID), power line communication (PLC),or a mobile cellular network such as a 3rd generation (3G) network, a4th generation (4G) network, a long term evolution (LTE) network, and a5th generation (5G) network.

FIG. 12 illustrates an example of a neural network (e.g., a neuralnetwork 1200).

Referring to FIG. 12 , the neural network 1200 may correspond to anexample of a deep neural network (DNN). For convenience of description,it is illustrated that the neural network 1200 includes two hiddenlayers, but the neural network may include a variety of hidden layers.In addition, although FIG. 12 shows that the neural network 1200includes a separate input layer 1210 for receiving input data, the inputdata may be directly input to the hidden layer.

Artificial nodes of layers other than an output layer in the neuralnetwork 1200 may be connected to artificial nodes of a next layerthrough links for transmitting an output signal. Through the links, anoutput of an activation function regarding weighted inputs of artificialnodes included in a previous layer may be input to the artificial node.The weighted input is an input (node value) of an artificial nodemultiplied by a weight, the input corresponds to axon values, and theweight corresponds to synaptic weights. The weight may be referred to asa parameter of the neural network 1200. The activation function mayinclude a sigmoid function, a hyperbolic tangent (tanh) function, and arectified linear unit (ReLU), and nonlinearity may be formed in theneural network 1200 by the activation function.

The in-memory processing device described above with reference to thedrawings may be used for in-memory processing or in-memory computing fordriving a deep learning algorithm. For example, the calculation of theweighted input transmitted between the nodes 1221 of the neural network1200 may be implemented by a MAC operation. The output from any one node1221 included in the neural network 1200 may be expressed as Equation 1below.

$\begin{matrix}{y_{i} = {f\left( {\sum\limits_{j = 1}^{m}{w_{j,i}x_{j}}} \right)}} & {{Equation}1}\end{matrix}$

Equation 1 may represent an output value y_(i) of the i-th node 1221form input values in a certain layer. In Equation 1, x_(j) may representthe output value of the j-th node of the previous layer, and w_(j,i) mayrepresent the weight applied to the output value of the j-th node andthe i-th node 1221 of the current layer. In Equation 1, f () mayrepresent an activation function. As shown in Equation 1, a resultobtained by multiplying the input value x_(j) by the weight w_(j,i) maybe used for the activation function. In other words, an operation (MACoperation) of multiplying and adding an appropriate input value x_(j)and weight w_(j,i) at a desired time point may be repeated. In additionto these uses, there are various application fields requiring MACoperations, and for this purpose, a neuromorphic device capable ofprocessing MAC operations in the analog domain may be used.

In the neural network 1200 composed of one or more layers including aplurality of nodes, the memory cells of the in-memory processing devicemay have a resistance corresponding to a connection weight of aconnection line connecting the nodes. An input signal provided alonginput lines (row lines) in which memory cells are arranged may representa value corresponding to a node value x_(j). Accordingly, the in-memoryprocessing device may perform at least some of the operations requiredto implement the neural network 1200.

On the other hand, the application of the in-memory processing device isnot necessarily limited to neuromorphic operations, but may also be usedfor operations that require fast processing of multiple input data usinganalog circuit characteristics with low power.

The two-dimensional array circuits, axon circuit groups, synapse arrays,neuron circuit groups, in-memory processing devices, switch groups,memory cell groups, column lines, voltage controlled delay lines,time-digital converters, resistors, output units, memory cell arrays,input lines, bias circuits, delay element circuits, buffer circuits,oscillators, counters, flip-flops, computing apparatuses, hostprocessors, RAMs, memory devices, sensor modules, communication modules,two-dimensional array circuit 20, axon circuit group 210, synapse array220, neuron circuit group 230, in-memory processing device 100, switchgroup 101, memory cell group 110, column line 120, voltage controlleddelay line 130, time-digital converter 140, memory cell group 510,resistors 520, voltage controlled delay lines 540, time-digitalconverter 550, output unit 560, memory cell array 590, input line 591,output line 592, voltage controlled delay line 600, voltage controlleddelay line 700, bias circuit 710, delay element circuit 720, delayelement circuit 730, buffer circuit 740, time-digital converter 800,oscillator 810, counter 820, flip-flops 832, 834, and 836, computingapparatus 1100, host processor 1110, RAM 1120, in-memory processingdevice 1130, memory device 1140, sensor module 1150, communicationmodule 1160, and other apparatuses, devices, units, modules, andcomponents described herein with respect to FIGS. 1-12 are implementedby or representative of hardware components. Examples of hardwarecomponents that may be used to perform the operations described in thisapplication where appropriate include controllers, sensors, generators,drivers, memories, comparators, arithmetic logic units, adders,subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1-12 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions used herein, which disclose algorithms forperforming the operations that are performed by the hardware componentsand the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access programmable readonly memory (PROM), electrically erasable programmable read-only memory(EEPROM), random-access memory (RAM), dynamic random access memory(DRAM), static random access memory (SRAM), flash memory, non-volatilememory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, blue-rayor optical disk storage, hard disk drive (HDD), solid state drive (SSD),flash memory, a card type memory such as multimedia card micro or a card(for example, secure digital (SD) or extreme digital (XD)), magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents.

What is claimed is:
 1. An in-memory processing apparatus, the apparatuscomprising: a memory cell array comprising memory cell groups configuredto generate current sums of column currents flowing through respectivecolumn lines in response to input signals input through row lines;voltage controlled delay circuits configured to output, in response toan input of a start signal at a first time point, stop signals at secondtime points delayed by delay times determined based on magnitudes ofapplied sampling voltages corresponding to the current sums; atime-digital converter configured to perform time-digital conversion atthe second time points; and sampling resistors connected to the columnlines, wherein the time-digital converter is configured to reset acounter at the first time point, and output counting values as digitalvalues at the second time points.
 2. The apparatus of claim 1, whereinthe voltage controlled delay circuits are configured to determine thedelay times in proportion to the magnitudes of the sampling voltages. 3.The apparatus of claim 1, wherein the voltage controlled delay circuitshave current-starved circuitry including transistors to which thesampling voltages are applied and inverters to which the start signal isinput.
 4. The apparatus of claim 1, wherein the voltage controlled delaycircuits include biases connected to the column lines and configured toapply the sampling voltages, current-starved delay elements to which thestart signals are input, and buffers configured to output the stopsignals.
 5. The apparatus of claim 1, wherein the sampling voltages areapplied to the sampling resistors.
 6. The apparatus of claim 5, whereinthe sampling resistors are connected in series to the column lines, andthe voltage controlled delay circuits are connected in parallel to thesampling resistors.
 7. The apparatus of claim 5, wherein values of thesampling voltages are determined based on composite resistances ofresistance values of the memory cell groups and resistance values of thesampling resistors.
 8. The apparatus of claim 1, further comprising adriver configured to input the input signals to the memory cell array ata third time point synchronized with the first time point.
 9. Theapparatus of claim 1, wherein the time-digital converter includes anoscillator configured to generate a pulse at the first time point andthe counter configured to count the pulse.
 10. The apparatus of claim 1,wherein the time-digital converter includes flip-flops configured tolatch the counting values at the second time point.
 11. An in-memoryprocessing method, the method comprising: inputting input signals tomemory cell groups through row lines of a memory cell array; applyingsampling voltages corresponding to current sums of column currentsflowing through column lines of the memory cell array to voltagecontrolled delay circuits connected to the column lines; inputting astart signal to the voltage controlled delay circuits at a first timepoint; outputting stop signals at second time points delayed by delaytimes determined based on magnitudes of the sampling voltages; andperforming time-digital conversion at the second time points, includingresetting a counter at the first time point, and outputting countingvalues as digital values at the second time points, wherein samplingresistors are connected to the column lines.
 12. The method of claim 11,wherein the method further comprises determining the sampling voltagesbased on composite resistances of resistance values of the memory cellgroups and resistance values of the sampling resistors.
 13. The methodof claim 11, further comprising outputting, based on the output countingvalues, an output digital value corresponding to a MAC operation resultof a neural network.
 14. An in-memory processing apparatus, theapparatus comprising: a memory cell array comprising memory cell groupseach corresponding to a respective column line and configured togenerate a current sum of column currents flowing through the respectivecolumn line in response to input signals applied through row lines;voltage controlled delay circuits each corresponding to a respectivecolumn line and configured to output, in response to an input of a startsignal at a first time point, a stop signal at second time point delayedby a delay time determined based on a magnitude of an applied samplingvoltage corresponding to a respective one of the current sums; atime-digital converter configured to, based on the stop signals, performtime-digital conversion at the second time points; and samplingresistors connected to the column lines, wherein the time-digitalconverter is configured to reset a counter at the first time point, andoutput counting values as digital values at the second time points. 15.The apparatus of claim 14, wherein the time-digital converter comprises:an oscillator configured to generate a counting pulse starting from thefirst time point, in response to receiving the start signal; the counterconfigured to generate the counting values of the counting pulse inresponse to the generating of the counting pulse; and flip-flops eachcorresponding to a respective column line and configured to output acounting value of the generated counting values corresponding to arespective second time point, in response to receiving a stop signal ofthe stop signals from a respective one of the voltage controlled delaycircuits.
 16. The apparatus of claim 14, wherein the sampling voltagesare applied to the sampling resistors.